In this paper, we have proposed DeepJSCC-Q, an end-to-end optimized joint source-channel coding scheme for wireless image transmission that is able to utilize a fixed channel input constellation and achieve similar performance to unquantized DeepJSCC, as previously proposed in [1]}.
Even with a constrained constellation, we are able to achieve superior performance to separation-based schemes using BPG for source coding and LDPC for channel coding, all the while avoiding the cliff-effect that plagues the separation-based schemes.
This makes the viability of DeepJSCC-Q in existing commercial hardware with standardized protocols much more attractive.
We also show that with sufficiently high modulation order, DeepJSCC-Q can approach the performance of DeepJSCC, which does not have a fixed channel input constellation.
As such, if such constellations are available on the hardware, DeepJSCC-Q can perform nearly as well as DeepJSCC.
Finally, we demonstrate that it is possible to learn a finite channel input alphabet that further improves the performance of DeepJSCC-Q, with the resultant constellation showing highly non-trivial geometry and distribution.
These promising results can bring DNN-based JSCC schemes closer to real world deployment.
